Linear and Nonlinear Programming, 2/e

Linear and Nonlinear Programming is considered a classic textbook in Optimization. While it is a classic, it also reflects modern theoretical insights. These insights provide structure to what might otherwise be simply a collection of techniques and results, and this is valuable both as a means for learning existing material and for developing new results. One major insight of this type is the connection between the purely analytical character of an optimization problem, expressed perhaps by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. This was a major theme of the first edition of this book and the second edition expands and further illustrates this relationship.
Linear and Nonlinear Programming covers the central concepts of practical optimization techniques. It is designed for either self-study by professionals or classroom work at the undergraduate or graduate level for technical students. Like the field of optimization itself, which involves many classical disciplines, the book should be useful to system analysts, operations researchers, numerical analysts, management scientists, and other specialists from the host of disciplines from which practical optimization applications are drawn.

Table of Contents:

1. Introduction. Part I: Linear Programming. 2. Basic Properties of Linear Programs.3. The Simplex Method. 4. Duality. 5. Transportation and Network Flow Problems. Part II: Unconstrained Problems. 6. Basic Properties of Solutions and Algorithms. 7. Basic Descent Methods. 8. Conjugate Direction Methods. 9. Quasi- Newton Methods. Part III: Constrained Minimization. 10. Constrained Minimization Conditions. 11. Primal Methods. 12. Penalty and Barrier Methods. 13. Dual and Cutting Plane Methods. 14. Lagrange Methods. Appendix A: Mathematical Review. A.1. Sets. A.2. Matrix Notation. A.3. Spaces. A.4. Eigenvalues and Quadratic Forms. A.5. Topological Concepts. A.6. Functions. Appendix B: Convex Sets. B.1. Basic Definitions. B.2. Hyperplanes and Polytopes. B.3. Separating and Supporting Hyperplanes. B.4. Extreme Points. Appendix C: Gaussian Elimination. Bibliography. Index.